The possible impact of weather on crimes in Amsterdam

import pandas as pd
import numpy as np
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import display, Markdown
import plotly.io as pio
pio.renderers.default = "notebook"
import plotly.graph_objects as go

Load data

You can put your data at the same directory as the notebook file and then use pandas to load the data.

df = pd.read_csv("merged_weather_misdrijven_monthly_v3.csv")
df

	year_month	DDVEC	FHVEC	FG	FHX	FHXH	FHN	FHNH	FXX	FXXH	...	3.5.2 Onder invloed (weg)	3.5.5 Weg (overig)	3.6.4 Aantasting openbare orde	3.7.1 Discriminatie	3.7.2 Vreemdelingenzorg	3.7.4 Cybercrime	3.9.1 Horizontale fraude	3.9.2 Verticale fraude	3.9.3 Fraude (overig)	Totaal misdrijven
0	2012-01	217.580645	58.709677	63.387097	89.032258	11.258065	36.451613	10.354839	136.129032	12.290323	...	228.0	48.0	90.0	4.0	21.0	3.0	108.0	7.0	211.0	8466.0
1	2012-02	194.551724	48.482759	51.448276	75.517241	11.655172	25.862069	11.965517	114.827586	11.206897	...	198.0	29.0	78.0	4.0	12.0	7.0	118.0	3.0	236.0	7374.0
2	2012-03	209.096774	33.709677	39.064516	60.645161	11.483871	18.709677	7.838710	92.903226	11.419355	...	305.0	31.0	116.0	4.0	14.0	16.0	147.0	9.0	272.0	8645.0
3	2012-04	190.233333	44.700000	50.700000	80.333333	11.833333	22.333333	7.866667	125.666667	13.100000	...	235.0	27.0	102.0	3.0	21.0	10.0	155.0	6.0	230.0	8143.0
4	2012-05	181.548387	37.258065	44.193548	68.064516	12.935484	18.709677	9.000000	110.322581	12.354839	...	237.0	28.0	101.0	6.0	20.0	4.0	136.0	6.0	256.0	8333.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
155	2024-12	197.096774	53.322581	58.451613	79.354839	12.000000	34.193548	8.870968	128.387097	12.612903	...	256.0	72.0	107.0	8.0	5.0	30.0	451.0	1.0	2.0	6871.0
156	2025-01	219.774194	41.129032	46.645161	74.516129	11.483871	20.645161	11.161290	117.096774	11.258065	...	190.0	78.0	126.0	5.0	4.0	52.0	457.0	2.0	5.0	6021.0
157	2025-02	130.678571	40.464286	44.178571	62.857143	8.571429	23.928571	9.535714	100.357143	11.035714	...	216.0	82.0	119.0	7.0	10.0	34.0	438.0	3.0	2.0	5824.0
158	2025-03	159.193548	31.516129	36.516129	57.419355	11.935484	16.451613	9.580645	88.709677	11.806452	...	244.0	100.0	123.0	4.0	8.0	38.0	442.0	1.0	6.0	6232.0
159	2025-04	121.633333	33.766667	38.733333	58.333333	13.400000	17.000000	7.533333	96.000000	14.033333	...	250.0	59.0	167.0	6.0	5.0	32.0	448.0	0.0	4.0	6794.0

160 rows × 95 columns

df.columns = df.columns.str.strip()
df['TX'] = df['TX'] / 10  # TX is in tienden van °C
df['TG'] = df['TG'] / 10  # TG is in tienden van °C
display(Markdown("### Bicycle Theft vs. Maximum Temperature"))
display(Markdown("_Slight increase in thefts when it's warmer._"))

# 📈 Plot maken
plt.figure(figsize=(8, 5))
sns.regplot(
    x='TX',
    y='1.2.3 Diefstal van brom-, snor-, fietsen',
    data=df,
    scatter_kws={'alpha': 0.5}
)
plt.xlabel("Maximum Temperature (°C)")
plt.ylabel("Number of Bicycle Thefts")
plt.grid(True)
plt.tight_layout()
plt.show()

# Uitleg onder de plot
caption = """
This scatterplot shows the relationship between monthly maximum temperature (°C) 
and the number of bicycle thefts reported in the Netherlands. Each point represents one month. 
The blue regression line indicates a slight upward trend: bicycle thefts appear to increase 
when temperatures are higher. A possible explanation is that warmer weather encourages 
more cycling and more bikes being left outside, creating more opportunities for theft.
"""
display(Markdown(caption))

Bicycle Theft vs. Maximum Temperature

Slight increase in thefts when it’s warmer.

This scatterplot shows the relationship between monthly maximum temperature (°C) and the number of bicycle thefts reported in the Netherlands. Each point represents one month. The blue regression line indicates a slight upward trend: bicycle thefts appear to increase when temperatures are higher. A possible explanation is that warmer weather encourages more cycling and more bikes being left outside, creating more opportunities for theft.

# Titel en korte take-away boven de plot
display(Markdown("### Weather vs. Water-Related Influence Reports"))
display(Markdown("_Higher temperature and evaporation coincide with more 'under influence on boat' incidents._"))

# 📈 Lijnplot maken
plt.figure(figsize=(10, 6))
plt.plot(df['year_month'], df['TX'], label='Max temperature (°C)', color='orange')
plt.plot(df['year_month'], df['EV24'], label='Evaporation (mm)', color='blue')
plt.plot(df['year_month'], df['3.4.2 Onder invloed (water)'], label='Under influence on boat', color='green')

plt.xlabel("Month")
plt.ylabel("Value")
plt.title("Temperature, Evaporation and Water-Influence Reports Over Time")
plt.xticks(rotation=45)
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()

# 📋 Caption onder de plot
caption = """
This multivariate line chart compares maximum temperature (°C), evaporation (mm), and the number of 
water-related incidents involving substance use (“under influence on water”) across months. 
The x-axis shows the months, while the y-axis reflects the respective values. The orange line shows 
temperature trends, blue shows evaporation, and green represents incident reports. The plot reveals that 
months with higher temperature and evaporation also see more such incidents. This pattern suggests 
that warm and dry conditions may encourage recreational activity on water, potentially increasing risky behavior.
"""
display(Markdown(caption))

Weather vs. Water-Related Influence Reports

Higher temperature and evaporation coincide with more ‘under influence on boat’ incidents.

This multivariate line chart compares maximum temperature (°C), evaporation (mm), and the number of water-related incidents involving substance use (“under influence on water”) across months. The x-axis shows the months, while the y-axis reflects the respective values. The orange line shows temperature trends, blue shows evaporation, and green represents incident reports. The plot reveals that months with higher temperature and evaporation also see more such incidents. This pattern suggests that warm and dry conditions may encourage recreational activity on water, potentially increasing risky behavior.

plt.figure(figsize=(10,6))

sns.regplot(data=df, x='VVN', y='1.1.1 Diefstal/inbraak woning', scatter_kws={'s':50}, line_kws={'color':'red'})

plt.title('Lower Visibility May Be Linked to More Home Burglaries')

plt.xlabel('Average Monthly Minimum Visibility (VVN scale, higher = better visibility)')
plt.ylabel('Total Burglary Incidents per Month')

plt.grid(True)

caption = (
    "This scatterplot with a regression line shows the relationship between average monthly minimum visibility "
    "(VVN) and the number of reported burglary incidents. The x-axis reflects visibility, with higher values indicating clearer conditions. "
    "The y-axis shows the total number of burglaries each month. A negative trend suggests that burglary incidents may increase "
    "in months with lower visibility, supporting the hypothesis that poor visibility creates favorable conditions for intruders."
)

plt.figtext(0.5, -0.1, caption, wrap=True, horizontalalignment='center', fontsize=10)

plt.show()

weer_vars = ['TG', 'RH', 'DR']
misdaad_vars = [
    '1.1.1 Diefstal/inbraak woning',
    '1.3.1 Ongevallen (weg)',
    '2.5.2 Winkeldiefstal',
    '1.2.4 Zakkenrollerij',
    '1.4.2 Moord, doodslag',
    '1.2.3 Diefstal van brom-, snor-, fietsen'
]

# Maak subset dataframe met weer en misdaad kolommen
df_subset = df[weer_vars + misdaad_vars]

# Bereken correlatie matrix
corr_matrix = df_subset.corr()

# Selecteer alleen correlaties tussen weer_vars (rijen) en misdaad_vars (kolommen)
corr_submatrix = corr_matrix.loc[weer_vars, misdaad_vars]

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(12, 6))
sns.heatmap(corr_submatrix, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation between Weather Variables and Crime Categories')
plt.xlabel('Crime Categories')
plt.ylabel('Weather Variables')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

# Map each month number to a season
season_map = {
    1: 'Winter', 2: 'Winter', 12: 'Winter',
    3: 'Spring', 4: 'Spring', 5: 'Spring',
    6: 'Summer', 7: 'Summer', 8: 'Summer',
    9: 'Autumn', 10: 'Autumn', 11: 'Autumn'
}

# Extract numeric month from 'year_month' and map to season
df['month'] = pd.to_datetime(df['year_month']).dt.month
df['Season'] = df['month'].map(season_map)

# Serious crime columns and their display names
serious_crimes = {
    '1.4.2 Moord, doodslag': 'Murder',
    '1.4.5 Mishandeling': 'Abuse',
    '3.7.4 Cybercrime': 'Cybercrime'
}

# Create pie charts
fig, axes = plt.subplots(1, 3, figsize=(18, 6))

for ax, (col, label) in zip(axes, serious_crimes.items()):
    # Total incidents per season for each crime
    season_totals = df.groupby('Season')[col].sum().reindex(['Winter', 'Spring', 'Summer', 'Autumn'])

    # Pie chart
    ax.pie(
        season_totals,
        labels=season_totals.index,
        autopct='%1.1f%%',
        startangle=90,
        colors=sns.color_palette("pastel")[0:4]
    )
    ax.set_title(f'Seasonal Distribution of {label}')

# Title and caption
fig.suptitle('Serious Crimes Occur Steadily Across All Seasons', fontsize=16)

caption = (
    "These pie charts show the distribution of monthly incidents of three serious crime categories—murder, abuse, and cybercrime—"
    "grouped by season. If these crimes were weather- or season-dependent, one would expect major seasonal differences. However, the pie charts "
    "reveal relatively even distributions across winter, spring, summer, and autumn, supporting the claim that these crimes are not influenced by weather or seasonality."
)

plt.figtext(0.5, -0.05, caption, wrap=True, horizontalalignment='center', fontsize=10)
plt.tight_layout()
plt.show()

# Create RH quartiles (low to high rainfall)
df['Rainfall_Level'] = pd.qcut(df['RH'], 4, labels=['Low', 'Medium-Low', 'Medium-High', 'High'])

# Set up figure
crime_columns = {
    '1.2.4 Zakkenrollerij': 'Pickpocketing',
    '1.4.6 Straatroof': 'Robbery',
    '1.4.3 Openlijk geweld (persoon)': 'Public Violence'
}

plt.figure(figsize=(18, 5))

for i, (col, label) in enumerate(crime_columns.items(), 1):
    plt.subplot(1, 3, i)
    sns.boxplot(data=df, x='Rainfall_Level', y=col, hue='Rainfall_Level', palette='Blues', legend=False)
    plt.title(f'{label} by Rainfall Level (RH Quartiles)')
    plt.xlabel('Rainfall Level (Based on RH Quartiles)')
    plt.ylabel('Monthly Incidents')
    plt.grid(True)

plt.suptitle('Monthly Street Crime Counts by Rainfall Level (Relative Humidity Quartiles)', fontsize=16, y=1.05)

caption = (
    "Boxplots comparing monthly crime incidents across four rainfall levels based on relative humidity (RH) quartiles. "
    "Each box shows the distribution of crime counts for one rainfall group: low, medium-low, medium-high, and high RH. "
    "If street crimes are more common during dry weather, you would expect to see higher medians and more outliers in the lower RH groups. "
    "This format helps detect patterns even when the overall correlation is weak."
)

plt.figtext(0.5, -0.1, caption, wrap=True, horizontalalignment='center', fontsize=10)
plt.tight_layout()
plt.show()

crime_columns = {
    'Pickpocketing': '1.2.4 Zakkenrollerij',
    'Robbery': '1.4.6 Straatroof',
    'Public violence': '1.4.3 Openlijk geweld (persoon)',
    'Under influence on boat': '3.4.2 Onder invloed (water)'
}

def create_slider_plot(df):
    temps = [round(t, 1) for t in list(frange(5.0, 26.5, 0.5))]
    fig = go.Figure()

    # Voeg alle frames toe, één per temperatuur
    frames = []
    for temp in temps:
        lower, upper = temp - 0.5, temp + 0.5
        filtered = df[(df['TX'] >= lower) & (df['TX'] < upper)]
        y = [ (filtered[col].sum() / df[col].sum()) * 100 if df[col].sum() > 0 else 0
              for col in crime_columns.values() ]
        frames.append(go.Frame(
            data=[go.Bar(x=list(crime_columns.keys()), y=y)],
            name=f"{temp}"
        ))

    # Voeg eerste data toe als initiele trace
    fig.add_trace(frames[0].data[0])

    # Zet layout, slider en frames
    fig.update_layout(
        title="Crime Distribution by Temperature",
        yaxis=dict(range=[0,25], title="Pct of Total Incidents"),
        xaxis_title="Crime Type",
        width=800, height=600,
        updatemenus=[dict(
            type="buttons",
            showactive=False,
            buttons=[dict(label="Play", method="animate",
                          args=[None, {"frame": {"duration": 300, "redraw": True},
                                       "fromcurrent": True, "transition": {"duration": 0}}])]
        )],
        sliders=[dict(
            active=temps.index(20.0),
            currentvalue={"prefix": "Temp: "},
            pad={"t": 50},
            steps=[dict(label=f"{t}°C", method="animate", args=[[str(t)], {"frame": {"duration": 0}, "mode": "immediate"}])
                   for t in temps]
        )]
    )
    fig.frames = frames
    return fig

def frange(start, stop, step):
    while start <= stop:
        yield round(start, 1)
        start += step

# Voorbeeldgebruik:
# df = pd.read_csv("crime_weather_merged.csv")
fig = create_slider_plot(df)
# fig.write_html("crime_temp_slider.html")
fig.show()